CFD simulations of a data center to train an artificial neural network model

Data centers, large facilities that host computing and networking equipment for dealing with large volumes of data, are the physical manifestation of the “cloud.” This study presents an experimentally validated room-level computational fluid dynamics (CFD) simulation of a raised-floor data center configuration consisting of one cold aisle with six racks on each side, and three computer room air conditioning units around the room periphery. Predictions from the finite-volume software package Future Facilities 6SigmaDCX, employing a pressure-based solver, are in good agreement with experimental measurements of total air flow rate and rack inlet temperatures, with average discrepancies less than 4% and 1.7 °C, respectively. The numerical predictions using this approach over a variety of operating conditions are used to train an artificial neural network (ANN)-based model to predict temperature and airflow distributions in near real time. The ANN model, with its rapid prediction capability, can then be used to develop a control framework to minimize power consumption in data centers, which accounts for more than 2% of total American electricity consumption.